Applied Machine Learning

Useful Links

• Canvas

• Course literature

---

Course Content

• Module 1: Learning from Data
• Module 2: Representing Data and Features
• Module 3: Supervised Machine Learning – Naive Bayes Classifiers, Ensembles of Decision Trees (Random Forests), and Support Vector Machines
• Module 4: Neural Networks and Deep Learning
• Module 5: Unsupervised Machine Learning – PCA, t-SNE, Agglomerative Clustering, and DBSCAN
• Module 6: Model Evaluation, Improvement, and Ethical Aspects

---

Assignments and Projects

• Written Assignment (INL1)
  Comparison of the performance of different supervised models
  Credits: 1.5

• Written Assignment (INL2)
  Problem solving with unsupervised learning
  Credits: 1

• Written Assignment (INL3)
  Neural Networks and Deep Learning
  Credits: 2

• Project (PRO1)
  Project Report
  Credits: 3

---